Description: This notebook contains the code used to find correlations in the SOTA 2022. The code is documented and shared to ensure full transparency of the process data is engineered and filtered. All relevant data and information is removed to ensure complete privacy of survey respondents.
Author: Chang Dakota Sum Kiu (schang23@andover.edu, dakotacsk@protonmail.com)
Editors: Raina Yang (ryang24@andover.edu)
Last Edited: 17-05-2022
What is your race? Check all that apply.
array([nan, 'Asian'], dtype=object)
array(['3 hours', '8 hours', nan, '4 hours', '5 hours', '2 hours',
'6 hours', '10 hours or more', '1 hour or less', '7 hours',
'9 hours'], dtype=object)
temp = df['#######'] for each in temp: print('df_' +each+ "= df[df['#######'] =='" + each + "']")
/Users/dcoder/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_Libertarian df_Unsure df_Liberal df_Socialist df_Independent df_nan df_Conservative df_Other (please specify) df_Communist
df_Moderately informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Moderately informed'] df_Slightly informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Slightly informed'] df_Extremely informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Extremely informed'] df_Very informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Very informed'] df_nan = df[df['How informed do you believe you are about politics and/or current events?'] =='nan'] df_Not at all informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Not at all informed']
df = df[df["Unnamed: 22"] =="Helicopter"] df = df[df["Unnamed: 24"] =="nan"] df = df[df["Unnamed: 25"] =="She/her/hers"] df = df[df["Unnamed: 26"] =="They/them/theirs"] df = df[df["Unnamed: 27"] =="Ze/hir/hirs"] df = df[df["Unnamed: 28"] =="Unsure"] df = df[df["Unnamed: 29"] =="Spavik"] df = df[df["Unnamed: 31"] =="Bisexual – sexually attracted to more than one gender"] df = df[df["Unnamed: 32"] =="Demisexual – sexually attracted only after developing an emotional connection"] df = df[df["Unnamed: 33"] =="nan"] df = df[df["Unnamed: 34"] =="Homosexual - sexually attracted to the same sex or gender"] df = df[df["Unnamed: 35"] =="Pansexual - sexually attracted to others, regardless of sex or gender"] df = df[df["Unnamed: 36"] =="Queer – an umbrella term used to describe a sexual orientation that does not conform to dominant societal norms"] df = df[df["Unnamed: 37"] =="Questioning"] df = df[df["Unnamed: 38"] =="Titties, Pussy"] df = df[df["Unnamed: 40"] =="Biromantic – romantically attracted to more than one gender"] df = df[df["Unnamed: 41"] =="Demiromantic – romantically attracted only after developing an emotional connection"] df = df[df["Unnamed: 42"] =="nan"] df = df[df["Unnamed: 43"] =="Homoromantic – romantically attracted to the same sex or gender"] df = df[df["Unnamed: 44"] =="Panromantic – romantically attracted to others, regardless of sex or gender"] df = df[df["Unnamed: 45"] =="Queer"] df = df[df["Unnamed: 46"] =="Questioning"] df = df[df["Unnamed: 47"] =="tall people six feet over"] df = df[df["Unnamed: 49"] =="Black"] df = df[df["Unnamed: 50"] =="Native Hawaiian or Pacific Islander"] df = df[df["Unnamed: 51"] =="Indigenous Peoples of the Americas"] df = df[df["Unnamed: 52"] =="nan"] df = df[df["Unnamed: 54"] =="Asian American"] df = df[df["Unnamed: 55"] =="Broadly European"] df = df[df["Unnamed: 56"] =="East Asian"] df = df[df["Unnamed: 57"] =="East European"] df = df[df["Unnamed: 58"] =="Ethnically Jewish"] df = df[df["Unnamed: 59"] =="Hispanic"] df = df[df["Unnamed: 60"] =="Latinx"] df = df[df["Unnamed: 61"] =="Mediterranean"] df = df[df["Unnamed: 62"] =="Middle Eastern"] df = df[df["Unnamed: 63"] =="Native American"] df = df[df["Unnamed: 64"] =="North African"] df = df[df["Unnamed: 65"] =="Pacific Islander"] df = df[df["Unnamed: 66"] =="Scandinavian"] df = df[df["Unnamed: 67"] =="South Asian"] df = df[df["Unnamed: 68"] =="Southeast Asian"] df = df[df["Unnamed: 69"] =="Sub-Saharan African"] df = df[df["Unnamed: 70"] =="West Indian/Caribbean"] df = df[df["Unnamed: 71"] =="nan"] df = df[df["Unnamed: 72"] =="Other"] df = df[df["Unnamed: 74"] =="Atheism"] df = df[df["Unnamed: 75"] =="Buddhism"] df = df[df["Unnamed: 76"] =="Catholicism"] df = df[df["Unnamed: 77"] =="Christianity"] df = df[df["Unnamed: 78"] =="Hinduism"] df = df[df["Unnamed: 79"] =="Islam"] df = df[df["Unnamed: 80"] =="Judaism"] df = df[df["Unnamed: 81"] =="Mormonism"] df = df[df["Unnamed: 82"] =="Paganism"] df = df[df["Unnamed: 83"] =="Protestantism"] df = df[df["Unnamed: 84"] =="nan"] df = df[df["Unnamed: 85"] =="I am kind of questioning my religion so I will still say that I’m Christian because I’ve identified with Christianity all my life but I still acknowledge that I’m going through a period of religious reflection and discovery."] df = df[df["Unnamed: 89"] =="Yes, from Andover"] df = df[df["Unnamed: 90"] =="nan"] df = df[df["Unnamed: 93"] =="I’m definitely left leaning, but I haven’t done enough research on political affiliations to be comfortable aligning with any yet."] df = df[df["Unnamed: 99"] =="AP"] df = df[df["Unnamed: 100"] =="BBC"] df = df[df["Unnamed: 101"] =="Buzzfeed"] df = df[df["Unnamed: 102"] =="CNBC"] df = df[df["Unnamed: 103"] =="CNN"] df = df[df["Unnamed: 104"] =="Daily Mail"] df = df[df["Unnamed: 105"] =="Financial Times"] df = df[df["Unnamed: 106"] =="Forbes"] df = df[df["Unnamed: 107"] =="nan"] df = df[df["Unnamed: 108"] =="New York Post"] df = df[df["Unnamed: 109"] =="NPR"] df = df[df["Unnamed: 110"] =="The Guardian"] df = df[df["Unnamed: 111"] =="nan"] df = df[df["Unnamed: 112"] =="nan"] df = df[df["Unnamed: 113"] =="The Washington Post"] df = df[df["Unnamed: 114"] =="Vox Media"] df = df[df["Unnamed: 115"] =="Not applicable (I don't read the news)"] df = df[df["Unnamed: 116"] =="phillipian"] df = df[df["Unnamed: 118"] =="Magazines"] df = df[df["Unnamed: 119"] =="Newspapers in print"] df = df[df["Unnamed: 120"] =="nan"] df = df[df["Unnamed: 121"] =="Podcasts/radio shows"] df = df[df["Unnamed: 122"] =="nan"] df = df[df["Unnamed: 123"] =="Television networks"] df = df[df["Unnamed: 124"] =="None"] df = df[df["Unnamed: 141"] =="Facebook"] df = df[df["Unnamed: 142"] =="nan"] df = df[df["Unnamed: 143"] =="Reddit"] df = df[df["Unnamed: 144"] =="Pinterest"] df = df[df["Unnamed: 145"] =="nan"] df = df[df["Unnamed: 146"] =="TikTok"] df = df[df["Unnamed: 147"] =="Twitter"] df = df[df["Unnamed: 148"] =="Tinder"] df = df[df["Unnamed: 149"] =="Tumblr"] df = df[df["Unnamed: 150"] =="VSCO"] df = df[df["Unnamed: 151"] =="WeChat"] df = df[df["Unnamed: 152"] =="Not applicable"] df = df[df["Unnamed: 153"] =="Youtube"] df = df[df["Unnamed: 158"] =="Gluten free"] df = df[df["Unnamed: 159"] =="Lactose Intolerant"] df = df[df["Unnamed: 160"] =="Pescetarian"] df = df[df["Unnamed: 161"] =="Religious (i.e. Kosher, Halal, etc.)"] df = df[df["Unnamed: 162"] =="Vegan"] df = df[df["Unnamed: 163"] =="Vegetarian"] df = df[df["Unnamed: 164"] =="None"] df = df[df["Unnamed: 165"] =="Egg fish allergy"] df = df[df["Unnamed: 183"] =="nan"] df = df[df["Unnamed: 184"] =="nan"] df = df[df["Unnamed: 185"] =="nan"] df = df[df["Unnamed: 186"] =="I do not have a support system on campus"] df = df[df["Unnamed: 187"] =="Prayer"] df = df[df["Unnamed: 191"] =="Digital (i.e. fingering, hand jobs, etc.)"] df = df[df["Unnamed: 192"] =="Oral"] df = df[df["Unnamed: 193"] =="Vaginal"] df = df[df["Unnamed: 194"] =="nan"] df = df[df["Unnamed: 204"] =="Yes, condoms"] df = df[df["Unnamed: 205"] =="Yes, finger condoms"] df = df[df["Unnamed: 206"] =="Yes, dental dams"] df = df[df["Unnamed: 207"] =="Yes, other"] df = df[df["Unnamed: 208"] =="No"] df = df[df["Unnamed: 209"] =="nan"] df = df[df["Unnamed: 234"] =="DXM"] df = df[df["Unnamed: 235"] =="DMT"] df = df[df["Unnamed: 236"] =="Heroin"] df = df[df["Unnamed: 237"] =="Ketamine"] df = df[df["Unnamed: 238"] =="LSD"] df = df[df["Unnamed: 239"] =="Mescaline peyote"] df = df[df["Unnamed: 240"] =="Methamphetamine"] df = df[df["Unnamed: 241"] =="MDMA (Molly)"] df = df[df["Unnamed: 242"] =="Opioids"] df = df[df["Unnamed: 243"] =="Psilocybin mushrooms"] df = df[df["Unnamed: 244"] =="Other"] df = df[df["Unnamed: 245"] =="nan"] df = df[df["Unnamed: 250"] =="School-sponsored event"] df = df[df["Unnamed: 251"] =="Classroom setting"] df = df[df["Unnamed: 252"] =="Other on-campus setting"] df = df[df["Unnamed: 253"] =="nan"] df = df[df["Unnamed: 261"] =="Computer Science"] df = df[df["Unnamed: 262"] =="nan"] df = df[df["Unnamed: 263"] =="nan"] df = df[df["Unnamed: 264"] =="Mathematics"] df = df[df["Unnamed: 265"] =="Music"] df = df[df["Unnamed: 266"] =="Natural Sciences"] df = df[df["Unnamed: 267"] =="nan"] df = df[df["Unnamed: 268"] =="Physical Education"] df = df[df["Unnamed: 269"] =="Statistics"] df = df[df["Unnamed: 270"] =="Theatre and Dance"] df = df[df["Unnamed: 271"] =="World Languages"] df = df[df["Unnamed: 272"] =="Not applicable (No unreasonable grading disparities exist at Andover)"] df = df[df["Unnamed: 297"] =="nan"] df = df[df["Unnamed: 298"] =="nan"] df = df[df["Unnamed: 299"] =="Gender"] df = df[df["Unnamed: 300"] =="Neurodivergence"] df = df[df["Unnamed: 301"] =="nan"] df = df[df["Unnamed: 302"] =="Race"] df = df[df["Unnamed: 303"] =="nan"] df = df[df["Unnamed: 304"] =="Sexual orientation"] df = df[df["Unnamed: 305"] =="Socioeconomic status"] df = df[df["Unnamed: 306"] =="I do not think that faculty hire should be based on diversity"] df = df[df["Unnamed: 308"] =="nan"] df = df[df["Unnamed: 309"] =="nan"] df = df[df["Unnamed: 310"] =="nan"] df = df[df["Unnamed: 311"] =="Club advisors"] df = df[df["Unnamed: 312"] =="unsure of the question "] df = df[df["Unnamed: 314"] =="Ability/Disability"] df = df[df["Unnamed: 315"] =="Ethnicity"] df = df[df["Unnamed: 316"] =="Gender"] df = df[df["Unnamed: 317"] =="Neurodiversities"] df = df[df["Unnamed: 318"] =="nan"] df = df[df["Unnamed: 319"] =="Race"] df = df[df["Unnamed: 320"] =="Religion"] df = df[df["Unnamed: 321"] =="Sexual orientation"] df = df[df["Unnamed: 322"] =="Socioeconomic status"] df = df[df["Unnamed: 323"] =="Unsure "] df = df[df["Unnamed: 325"] =="nan"] df = df[df["Unnamed: 326"] =="nan"] df = df[df["Unnamed: 327"] =="Gender"] df = df[df["Unnamed: 328"] =="nan"] df = df[df["Unnamed: 329"] =="nan"] df = df[df["Unnamed: 330"] =="Race"] df = df[df["Unnamed: 331"] =="Religion"] df = df[df["Unnamed: 332"] =="Sexual orientation"] df = df[df["Unnamed: 333"] =="Socioeconomic status"] df = df[df["Unnamed: 334"] =="Connections"] df = df[df["Unnamed: 336"] =="Ability/Disability"] df = df[df["Unnamed: 337"] =="Ethnicity"] df = df[df["Unnamed: 338"] =="Gender"] df = df[df["Unnamed: 339"] =="Neurodivergence"] df = df[df["Unnamed: 340"] =="Political beliefs"] df = df[df["Unnamed: 341"] =="Race"] df = df[df["Unnamed: 342"] =="Religion"] df = df[df["Unnamed: 343"] =="Sexual orientation"] df = df[df["Unnamed: 344"] =="Socioeconomic status"] df = df[df["Unnamed: 345"] =="anxiety"] df = df[df["Unnamed: 347"] =="Ability/Disability"] df = df[df["Unnamed: 348"] =="Ethnicity"] df = df[df["Unnamed: 349"] =="Gender"] df = df[df["Unnamed: 350"] =="Neurodivergence"] df = df[df["Unnamed: 351"] =="nan"] df = df[df["Unnamed: 352"] =="Race"] df = df[df["Unnamed: 353"] =="Religion"] df = df[df["Unnamed: 354"] =="Sexual orientation"] df = df[df["Unnamed: 355"] =="Socioeconomic status"] df = df[df["Unnamed: 357"] =="Ability/Disability"] df = df[df["Unnamed: 358"] =="Ethnicity"] df = df[df["Unnamed: 359"] =="Gender identity"] df = df[df["Unnamed: 360"] =="Neurodivergence"] df = df[df["Unnamed: 361"] =="Political belief"] df = df[df["Unnamed: 362"] =="Race"] df = df[df["Unnamed: 363"] =="Religion"] df = df[df["Unnamed: 364"] =="Sexual orientation"] df = df[df["Unnamed: 365"] =="Socioeconomic status"] df = df[df["Unnamed: 366"] =="body image"] df = df[df["Unnamed: 372"] =="Warning"] df = df[df["Unnamed: 373"] =="Probation"] df = df[df["Unnamed: 374"] =="nan"]
bisexaul out of she/her: 113 out of 471 , percentage : 23.991507430997878 % demisexaul out of she/her: 8 out of 471 , percentage : 1.6985138004246285 % heterosexual out of she/her: 276 out of 471 , percentage : 58.59872611464968 % homosexual out of she/her: 19 out of 471 , percentage : 4.033970276008493 % pansexual out of she/her: 17 out of 471 , percentage : 3.6093418259023355 % queer out of she/her: 47 out of 471 , percentage : 9.978768577494693 % questioning out of she/her: 52 out of 471 , percentage : 11.040339702760086 % bisexaul out of he/him: 36 out of 446 , percentage : 8.071748878923767 % demisexaul out of he/him: 5 out of 446 , percentage : 1.1210762331838564 % heterosexual out of he/him: 370 out of 446 , percentage : 82.95964125560538 % homosexual out of he/him: 22 out of 446 , percentage : 4.932735426008969 % pansexual out of he/him: 7 out of 446 , percentage : 1.5695067264573992 % queer out of he/him: 12 out of 446 , percentage : 2.690582959641256 % questioning out of he/him: 10 out of 446 , percentage : 2.242152466367713 %
df_Asian : 380 No 212 Yes 168 Name: Are you a varsity athlete?, dtype: int64 df_Black : 110 No 60 Yes 50 Name: Are you a varsity athlete?, dtype: int64 df_NHPI : 12 Yes 7 No 5 Name: Are you a varsity athlete?, dtype: int64 df_Indigenous : 26 No 18 Yes 8 Name: Are you a varsity athlete?, dtype: int64 df_White : 543 Yes 284 No 259 Name: Are you a varsity athlete?, dtype: int64
df_Asian : 380 Yes 285 No 73 Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64 df_Black : 110 Yes 100 No 6 Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64 df_NHPI : 12 Yes 8 No 3 Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64 df_Indigenous : 26 Yes 24 No 1 Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64 df_White : 543 Yes 423 No 89 Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64
df_Asian : 380 No 245 Yes 116 Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64 df_Black : 110 No 95 Yes 11 Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64 df_NHPI : 12 No 8 Yes 3 Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64 df_Indigenous : 26 No 19 Yes 6 Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64 df_White : 543 No 335 Yes 177 Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64
4 hours 248 3 hours 207 5 hours 161 2 hours 83 6 hours 71 7 hours 25 1 hour or less 19 8 hours 15 10 hours or more 15 9 hours 3 Name: How many hours do you spend on coursework outside of class each day?, dtype: int64
3.662133891213389
df_bi : 156 Yes 128 No 18 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64 df_demi : 18 Yes 15 No 1 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64 df_het : 653 Yes 455 No 134 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64 df_homo : 47 Yes 37 No 6 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64 df_pan : 31 Yes 26 No 4 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64 df_queer : 71 Yes 55 No 8 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64 df_questioning : 64 Yes 54 No 8 Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64
2022 and anal: 21 out of 238 , percentage : 8.823529411764707 % 2023 and anal: 7 out of 265 , percentage : 2.6415094339622645 % 2024 and anal: 7 out of 265 , percentage : 2.6415094339622645 % 2025 and anal: 1 out of 184 , percentage : 0.5434782608695652 % 2022 and digital: 121 out of 238 , percentage : 50.84033613445378 % 2023 and digital: 97 out of 265 , percentage : 36.60377358490566 % 2024 and digital: 66 out of 265 , percentage : 24.90566037735849 % 2025 and digital: 20 out of 184 , percentage : 10.869565217391305 % 2022 and oral: 119 out of 238 , percentage : 50.0 % 2023 and oral: 88 out of 265 , percentage : 33.20754716981132 % 2024 and oral: 61 out of 265 , percentage : 23.0188679245283 % 2025 and oral: 14 out of 184 , percentage : 7.608695652173914 % 2022 and vaginal: 93 out of 238 , percentage : 39.075630252100844 % 2023 and vaginal: 57 out of 265 , percentage : 21.50943396226415 % 2024 and vaginal: 31 out of 265 , percentage : 11.69811320754717 % 2025 and vaginal: 7 out of 184 , percentage : 3.804347826086957 %
df_bi : 156 Not applicable, I have never engaged in sexual activity 80 Several times a week 16 Weekly 16 Several times a year 15 About once per month 10 Once a year or less 9 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64 df_demi : 18 Not applicable, I have never engaged in sexual activity 11 Several times a year 2 Weekly 2 Several times a week 1 Once a year or less 1 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64 df_het : 653 Not applicable, I have never engaged in sexual activity 348 Weekly 60 Several times a year 60 About once per month 45 Several times a week 44 Once a year or less 22 Daily 12 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64 df_homo : 47 Not applicable, I have never engaged in sexual activity 31 Several times a week 4 Several times a year 3 Once a year or less 2 Weekly 2 About once per month 1 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64 df_pan : 31 Not applicable, I have never engaged in sexual activity 18 Once a year or less 4 Several times a week 3 About once per month 3 Several times a year 2 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64 df_queer : 71 Not applicable, I have never engaged in sexual activity 39 Once a year or less 7 About once per month 6 Several times a year 5 Several times a week 4 Weekly 3 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64 df_questioning : 64 Not applicable, I have never engaged in sexual activity 43 About once per month 7 Weekly 4 Once a year or less 3 Several times a year 2 Daily 2 Several times a week 2 Name: How frequently, on average, do you engage in sexual activity?, dtype: int64
df_Asian : 380 No 154 Somewhat 147 Yes 41 Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64 df_Black : 110 No 55 Somewhat 41 Yes 4 Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64 df_NHPI : 12 Somewhat 3 Yes 3 No 3 Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64 df_Indigenous : 26 No 14 Somewhat 8 Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64 df_White : 543 Somewhat 185 No 172 Yes 122 Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64
Number of NB people who think faculty gender diversity is not enough: 7 out of 17 , percentage : 41.17647058823529 % Number of Men who think faculty gender diversity is not enough: 45 out of 426 , percentage : 10.56338028169014 % Number of Women who think faculty gender diversity is not enough: 90 out of 458 , percentage : 19.65065502183406 %
Number of df_Asian who think faculty racial diversity is not enough: 140 out of 380 , percentage : 36.84210526315789 % Number of df_Black who think faculty racial diversity is not enough: 74 out of 110 , percentage : 67.27272727272727 % Number of df_NHPI who think faculty racial diversity is not enough: 3 out of 12 , percentage : 25.0 % Number of df_Indigenous who think faculty racial diversity is not enough: 18 out of 26 , percentage : 69.23076923076923 % Number of df_White who think faculty racial diversity is not enough: 167 out of 543 , percentage : 30.755064456721914 %
df_socialDivideNo : 49 Upper-middle class 19 Upper class 16 Middle class 11 Lower-middle class 3 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideAbility : 225 Upper-middle class 93 Upper class 66 Middle class 38 Lower class 14 Lower-middle class 14 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideEthnicity : 420 Upper-middle class 172 Upper class 126 Middle class 67 Lower-middle class 33 Lower class 21 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideGender : 372 Upper-middle class 158 Upper class 121 Middle class 49 Lower-middle class 25 Lower class 19 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideNeuroDiv : 200 Upper-middle class 76 Upper class 69 Middle class 32 Lower class 12 Lower-middle class 11 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDividePolitics : 523 Upper-middle class 219 Upper class 166 Middle class 84 Lower-middle class 31 Lower class 22 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideRace : 556 Upper-middle class 232 Upper class 170 Middle class 90 Lower-middle class 40 Lower class 23 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideReligion : 115 Upper-middle class 46 Upper class 32 Middle class 20 Lower class 10 Lower-middle class 7 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideSexualOrientation : 482 Upper-middle class 201 Upper class 152 Middle class 73 Lower-middle class 31 Lower class 25 Name: What is your perceived socioeconomic status?, dtype: int64 df_socialDivideSEC : 535 Upper-middle class 225 Upper class 164 Middle class 83 Lower-middle class 39 Lower class 24 Name: What is your perceived socioeconomic status?, dtype: int64
Number of df_Asian who think there is social divide due to race: 237 out of 380 , percentage : 62.36842105263158 % Number of df_Black who think there is social divide due to race: 77 out of 110 , percentage : 70.0 % Number of df_NHPI who think there is social divide due to race: 6 out of 12 , percentage : 50.0 % Number of df_Indigenous who think there is social divide due to race: 19 out of 26 , percentage : 73.07692307692307 % Number of df_White who think there is social divide due to race: 294 out of 543 , percentage : 54.14364640883977 %
Women who think gender affects ability to obtain leadership pos: 131 out of 458 , percentage : 28.602620087336245 % Men who think gender affects ability to obtain leadership pos: 80 out of 426 , percentage : 18.779342723004692 % NBs who think gender affects ability to obtain leadership pos: 6 out of 17 , percentage : 35.294117647058826 %
Number of df_Asian who think race affects ability to obtain leadership pos: 131 out of 380 , percentage : 34.473684210526315 % Number of df_Black who think race affects ability to obtain leadership pos: 41 out of 110 , percentage : 37.27272727272727 % Number of df_NHPI who think race affects ability to obtain leadership pos: 0 out of 12 , percentage : 0.0 % Number of df_Indigenous who think race affects ability to obtain leadership pos: 10 out of 26 , percentage : 38.46153846153847 % Number of df_White who think race affects ability to obtain leadership pos: 130 out of 543 , percentage : 23.941068139963168 %
Unsure Income vs ability to gain Leadership Pos IS affected by SEC: 19 out of 230 , percentage : 8.26086956521739 % 0-34k Income vs ability to gain Leadership Pos IS affected by SEC: 8 out of 27 , percentage : 29.629629629629626 % 35-59k Income vs ability to gain Leadership Pos IS affected by SEC: 9 out of 32 , percentage : 28.125 % 60-99k Income vs ability to gain Leadership Pos IS affected by SEC: 13 out of 56 , percentage : 23.214285714285715 % 100-149k Income vs ability to gain Leadership Pos IS affected by SEC: 16 out of 79 , percentage : 20.253164556962027 % 150-249k Income vs ability to gain Leadership Pos IS affected by SEC: 13 out of 117 , percentage : 11.11111111111111 % 250-499k Income vs ability to gain Leadership Pos IS affected by SEC: 24 out of 149 , percentage : 16.10738255033557 % 500+k Income vs ability to gain Leadership Pos IS affected by SEC: 40 out of 261 , percentage : 15.32567049808429 %
Number of df_Asian who think their race affects their comfort level in classroom: 115 out of 380 , percentage : 30.263157894736842 % Number of df_Black who think their race affects their comfort level in classroom: 77 out of 110 , percentage : 70.0 % Number of df_NHPI who think their race affects their comfort level in classroom: 2 out of 12 , percentage : 16.666666666666664 % Number of df_Indigenous who think their race affects their comfort level in classroom: 14 out of 26 , percentage : 53.84615384615385 % Number of df_White who think their race affects their comfort level in classroom: 83 out of 543 , percentage : 15.285451197053407 %
Women who think gender affects ability to obtain leadership pos: 160 out of 458 , percentage : 34.93449781659388 % Men who think gender affects ability to obtain leadership pos: 49 out of 426 , percentage : 11.502347417840376 % NBs who think gender affects ability to obtain leadership pos: 11 out of 17 , percentage : 64.70588235294117 %